Transcript jeremy-nicholson - New England Drug Metabolism Discussion

NEDMDG SYMPOSIUM
Worcester, Mass. June 8th, 2006
STATISTICAL SPECTROSCOPY AND GLOBAL
SYSTEMS BIOLOGY APPROACHES IN
DISEASE MODELING
Jeremy K. Nicholson, PhD
Professor and Head of Biological Chemistry
Imperial College
University of London
Summary
• The quest for new drugs - ‘Top-down’ Systems
Biology, gene-environment interactions and the
Personalized Healthcare Paradigm.
• Generating and modeling system level metabolic data
in experimental disease states.
– Pharmaco-metabonomics and predictive models
– Biomarker recovery via statistical spectroscopy
• NMR, UPLC-MS, UPLC-MS/NMR, Proteo-metabonomics
• Characterizing genetic,dietary and microbial
contributions to Human Metabolic Phenotypes:
Molecular epidemiology and “The Health of Nations”.
Can Systems Biology save the
Pharmaceutical Industry?
High technology has generally
not helped the rate of practical
drug discovery!
ONLY 11% OF DRUGS IN CLINICAL
TRIALS MAKE IT TO MARKET!
PHARMACEUTICAL PRODUCTIVITY DECLINE: REAL COST
PER DRUG HAS INCREASED 10 FOLD IN 30 YEARS!
THIS IS AN UNSUSTAINABLE BUSINESS MODEL!
23,710
3,051
HUMAN GENOME
23,710
Lipinski Rules: Compound
Bioavailability is poor if…
> 5 H-bond donors
CLog P > 5
Sum {N + O} > 10
Mass > 500 DALTONS
DRUGGABLE
GENOME
3,000
DRUG
TARGETS
1000
DISEASE
MODIFYING
GENES
3,000
PHARMACEUTICAL PIPELINE ATTRITION
DISCOVERY
LIBRARY (NME)
DEVELOPMENT
INVESTIGATIONAL NEW DRUG
ADME/ANIMALS
CLINICAL TRIALS
Screening Studies:
EARLYProteomic and
Transcriptomic,
Metabolomic/Metabonomic
Chemistry
and
in vitro screens
Pre-Lead Prioritization
Systems Biology Application
PHARMACOLOGY/TOXICOLOGY
NEW DRUG
TARGETS
MARKET
Ph1
Ph2 CLINICAL
Ph3
DIAGNOSTIC
MARKERS
Response Analysis:
Genotyping,
CRITICAL
Proteomics,
Pharmaco-genomics.
Pharmaco-metabonomics
$$$ failure!
Product Rescue?
Preclinical Safety
Biomarkers and Mechanisms
OBLIGATE
Clinical Safety
BiomarkersFATAL
Quasi-Darwinian
Lead Selection Clinical Efficacy
Withdrawal
Selection
Preclinical Efficacy
Models & Biomarkers
and Optimization Biomarkers
from
© Imperial College, 2006
market
Personalized Healthcare
Optimized drug
efficacy & minimized
toxicity
Theranostics
& patient
stratification
Optimized
Nutrition
Global Systems Biology
COMPREHENSIVE PHENOTYPE
Metabonomics
Genomics
Microbiome
© Imperial College,2006
Proteomics
Xeno-metabolome
Multivariate Descriptions of Metabolism
• MetaboLome Definition: The quantitative
analyisis or description of all low molecular
weight metabolites in specified cellular,
tissue or biofluid compartments.
(Metabolomics: Numbers, chemical classes, structures,
concentrations: < 1KDa)
• MetaboNome Definition: The sums,
products & interactions of all the individual
compartments/metabolomes (including
extra-genomic sources) dispersed in a
complex organism…The ‘Global’ System.
METABONOMICS
“Quantitative measurement of multivariate metabolic
responses of multicellular systems to pathophysiological
stimuli or genetic modification”
(AIMS TO MODEL GLOBAL METABOLIC
REGULATION OF COMPLEX SYSTEMS INCLUDING DYNAMIC
INTERACTIONS & COMPARTMENTALIZATION OF COMPONENTS)
METABOLOMICS (various definitions)
e.g. “measurement of metabolite concentrations
& fluxes in cell systems”.
OR “measurement & modelling of all metabolites &
pathways in a system”
C1
METABONOMICS OF
COMPLEX SYSTEMS
C2
Cellular transcriptome
Multiple cell lines
C1,C2…C8 etc.
Cellular proteome
Intracellular metabolome
Intervention
C8
with
C3
specific
C7
Extra-cellular metabolite
pool (biofluids)
C4
target
Extra-cellular metabolite pool
Tissue profiles
Excretion signatures
Molecular compartments
Reaction profile
measurement & modeling
External secretions /excretion
C6
C5
PHYSICAL-BIOCHEMICAL
FEATURES
BIOLOGICAL FLUID TYPES
OF URINE AND PLASMA
(primary secretory and connective roles)
URINE: Variable pH, ionic strength, osmolarity.
Key Diagnostic
Fluids:constant.
Plasma, Urine.
High dielectric
Extreme dynamic concentration range (>1011).
time
Specialized
Functions:
Cerebrospinal,
thyroid.
Thousands
of molecules
< 1KDa, (polarity?).
averaged
Metal complexes
and supramolecular
Saliva (sub-lingual,
parotid,
sub-maxillary),aggregates.
Gastric,
Many small proteins, high enzyme activities in
Bile, Pancreatic.
pathological states-dynamically reactive matrix.
Amniotic, Follicular, Milk, Seminal Vesicle, Prostatic,
Epidydimal, Seminal.
PLASMA: Relatively constant pH, ionic strength, osmolarity.
LowerFluids:
bulk dielectric
Pathological
Ascites,constant.
Cystic, Blister.
5).
High
dynamic
concentration
range
(>10
snap
Artificial Hundreds
Fluids: Bronchiolar
lavage
fluid,and
peritoneal
of
of
molecules
<
1KDa
>1KDa.
shot
dialysates,
hemodialysates,
fecal water, rectal
Metal
complexes and supramolecular
complexes.
dialysates,
cell extracts and
cell diffusionalsupernatants.
Multi-compartment
-multi
matrix
Many large proteins and protein complexes.
Analytical Approaches in Metabonomics
and Metabolomics
NMR Spectroscopy
Biofluids, extracts, cells/tissues
Mass Spectrometry
Biofluids and extracts
Linked chromatography/MS
Single pulse 1H, 13C, 31P
Many Ionization Methods
2+ D methods
COSY
(pattern recognition for
classification,
TLC/MS
TOCSY
Single quad
CE-MS
HMQC
Triple quad
GC-MS
HMBC etc
TOF-MS
LC-NMR, CE-NMR, CEC-NMR
HPLC-MS
QTOF-MS
PFG Diffusion analysis
Ion trap
UPLC-MSn
DOSY etc…
Linear ion trap
LC-ICPMS-MS
HR-MAS
(cells
+
tissues)
FTMS
(Linking multiple spectra & spectral types for
PFG-MAS
LC- NMR -MSn
structure
(cryoprobes/robotic
FI etc)elucidation/pathway analysis)
CHEMOMETRIC MODELLING
diagnostics & biomarker analysis)
STATISTICAL SPECTROSCOPY
Standard Analytical Information: Identity, Structure, Quantity (BOTH)
Physical Biochemical Information: Interactions, Compartments (NMR)
CHEMOMETRIC TOOLS FOR
INFORMATION RECOVERY FROM
MULTIVARIATE DATA
UNSUPERVISED
• Principal Components
Analysis (PCA)
• Hierarchichal Cluster
Analysis (HCA)
• Logical blocking-PCA
• Non-linear Mapping (NLM)
• Supergravity Association
Mapping (SAM)….etc.
© Imperial College, 2006
SUPERVISED
• Partial Least Squares (PLS) &
PLS-DA
• O-PLS & O2-PLS
• Soft Independent Modeling of
Class Analogy (SIMCA)
• Rule Induction
• Bayes Nets/Machine Learning
• Genetic Algorithms
• Neural Networks
• CLOUDS…etc…
900 MHz 1H NMR Spectrum of Untreated Human Urine
Contains Latent
Biomarker information on:
Genotype
Physiological state
Nutritional state
Gut microbes
‘Biological’ Age
Presence of Disease
Translatable Biomarkers
Diagnostic
Prognostic
Toxicity
Efficacy
Primary and
co-metabolome interactions
in mammalian systems
(Nicholson et al Nature, Microbiology, 2005, 3, 2-8)
GUT ‘MICROBIOME’
1
2
3
4
5
6
Species transcriptomes
Species proteomes
HOST GENOME
A
B
C
D
E
F
Cellular transcriptomes
Species metabolomes
Enteron
microbial and dietary
2o metabolites
Cellular proteomes
1o Intracellular metabolomes
Extracellular metabolite pool
Secretory Metabolomes
Humans: > 500 functionally distinct
NORMAL cell types/ca.10 trillion
parenchymal cells
Co-metabolome enters
via hepatic portal
+ mesenteric veins
Biliary secretions enter
duodenum from
common bile duct
ENTEROHEPATIC
CIRCULATION
Humans: > 1000 Species.
> 100 trillion cells
MICROBIAL-MAMMALIAN CO-METABOLISM OF CHOLIC ACID
OH
1. Biosynthesis: cholic acid
COOH
HO
OH
2. Phase II glycine Conjugation
Phase II taurine Conjugation
OH
OH
C O
N
H
COOH
MAMMALIAN LIVER
HO
7. Phase II glycine Conjugation
OH
H
H
HO
Phase II taurine Conjugation
OH
taurocholic acid
glycocholic acid
3. Secretion into bile
H
C O
N
H
SO3H
C O
N
COOH
HO
H
C O
N
SO3H
HO
taurodeoxyocholic acid
glycodeoxyocholic acid
8. Secretion into bile
enterohepatic
circulation
amino acid deconjugation
by gut microbiota
6. reabsorption into blood
via hepatic portal system
OH
4. Regeneration of
cholic acid
9. Deconjugation further reactions
H
COOH
deoxycholic acid
COOH
MAMMALIAN GUT
HO
OH
5. 77-a-dehydroxylation
by gut microbiota
a-
HO
De-conjugated bile acids are
less efficient at emulsifying fats
PHARMACO-METABONOMICS
A new paradigm for
personalized predictive drug
metabolism and toxicology.
Definition: The prediction of the quantitative
outcome of an intervention based on a pretreatment metabolic model: Applications in drug
metabolism, xenobiotic toxicity, drug efficacy…etc.
Understanding drug interaction responses in relation to individual
metabolic variation: Gene-environment interactions determine the
pre-dose starting phenotype.
Genetic Factors
P450 Polymorphisms
SNP variations
Nutrition
Gut Microbiome
Age
Hormonal status
Are there locations that are more risk averse
For particular interventions?
Prognostic biomarker clusters?
Individual (dot) location is the
resultant of the influence
vectors in m-space.
Clayton et al 440 (20)
1073-1077, 2006)
Global System Interactions Affecting Drug Metabolism & Toxicity
Conditional
Metabolic
Phenotype
Host Genetic Constitution
Interspecies variations
& individual SNP variations
Individual Gut Microbiome
microbial species variation
& ACTIVITY
Specific drug metabolizing
enzyme complement
(CYP450) polymorphisms
Tissue-specific CYP450
induction state
(e.g., in liver & gut)
Nutritional status
& dietary composition
Metabolic Fate
Nicholson, JK et al
and Toxicity
Nature, Biotechnology
of Drug
22 (10) 1268-1274. (2004)
STATISTICAL SPECTROSCOPY
“The application of multivariate statistical
methods to extract latent structural or
connectivity information in multiple spectral
data sets from samples or experiments
collected serially or in parallel.”
1. STATISTICAL SEARCH SPACE
REDUCTION FOR BIOMARKER
IDENTIFICATION IN SERIAL UPLC-MS
DATA SETS
Crockford et al Analytical Chemistry (2006), in press
PARTIAL LEAST SQUARES DISCRIMINANT ANALYSIS
(PLS-DA) SORTS FEATURES ACCORDING TO IMPORTANCE
FOR CLASS SEPARATION.
SPECTRAL LOADINGS BACK-PROJECTED DIRECTLY TO LCMS CHROMATOGRAM TO IDENTIFY RETENTION TIMES &
MASSES OF CANDIDATE BIOMARKERS.
O-PLS-DA STATISTICAL SEARCH SPACE REDUCTION
Hydrazine dosed (10) vs control (10):
Statistically significant peaks (r > 0.6)
Back-projected into UPLC-MS time domain
356 features
© IMPERIAL COLLEGE 2006
O-PLS-DA STATISTICAL SEARCH SPACE REDUCTION
r > 0.8
STRONG CANDIDATE
BIOMARKERS
51 features
© Imperial College, 2006
STOCSY
•
RECONSTRUCTION OF LATENT BIOMARKER INFORMATION
FROM LARGE SPECTROSCOPIC SETS BY STATISTICAL
TOTAL CORRELATION SPECTROSCOPY (STOCSY)
Cloarec et al Analytical Chemistry, 77 (5) 1282-1289, 2005.
Calculate correlation matrix (C) between all computer points
(d/d) for all 1D spectra in all datasets to be compared:
X1 and X2 are the auto-scaled experimental matrices of n x v1 and n x v2
n = number of spectra in each class
v1 and v2 = number of variables in each matrix (32K)
2D STOCSY: Plot d/d correlation matrix for all
samples, color code by r2. Gives both self-molecular
correlations (assignment) and also pathway and
compartment correlations.
R-Selected 2D STOCSY (30 x 1D mouse urine spectra)
Only self molecular correlations r2 > 0.9 plotted
SHY
STATISTICAL HETEROSPECTRSCOPY
Analytical Chemistry (2006) 78 363-371.
SHY
Parallel NMR
& MS data
Collection
Sequential
NMR & UPLC-MS
spectra can be
obtained on each
sample for
statistical integration
© Imperial College, 2006
NMR Control Rat urine
Hydrazine treated Rat
UPLC-MS Control Rat urine
Hydrazine treated Rat
SHY CONNECTIONS IN PARALLEL
NMR-MS SPECTROSCOPIC SETS
Direct Structure Assignment Co-variance
dX-Ym/z (parent)-Zm/z (fragment)
MS
data
Zm/z
Am/z
Ym/z
dX
dB
NMR
data
Direct Pathway Connection Co-variance (Am/z- dB)
Statistical HeterospectroscopY (SHY): Expansion- shows
NMR to parent ion, fragment pattern & pathway correlates.
•
m/z
N-acetyl-lysine
NMR domain
Correlation/anticorrelation coefficients
MS
domain
MOLECULAR EPIDEMIOLOGY
DATA DRIVEN TOP-DOWN
SYSTEM METABOLIC MODELING
Can genetic, dietary, microbial, and
environmental influences in large scale
population studies be deconvolved?
Examples from the INTERMAP and
INTERSALT studies.
J. Stamler (PI)
P. Elliot (PI)
M. Daviglus
H. Kesteloot
H. Ueshima
B. Zhou
Q. Chan
M. D Iorio
E. Maibaum
S. Bruce
C. Teague
R.L. Loo
L. Smith
Acknowledgements:
The INTERMAP study has been supported by Grant 5RO1-HL50490-09, 5-RO1 HL65461-04 and 5 RO1
HL71950-02 from the US National Heart, Lung, and
Blood Institutes, National Institutes of Health, Bethesda,
MD, USA; by the Chicago Health Research Foundation;
and by national agencies in Japan, People’s Republic
of China and the United Kingdom.
INTERSALT Study was supported by the Council on
Epidemiology and Prevention of the International
Society and Federation of Cardiology; World Health
Organisation; International Society of Hypertension;
Wellcome Trust; National Heart, Lung, and Blood
Institute, US; Heart Foundations of Canada, Great
Britain, Japan and the Netherlands; Chicago Health
Research Foundation; Parastatal Insurance, Company,
Belgium; and by many national agencies supporting
local studies.
FUNDAMENTAL METABOTYPE DIFFERENCES
PCA-DA of population data (disease outliers removed)
JAPANESE
•
AMERICAN
CHINESE
© Imperial College, 2006
COMBINATION OF GENETIC ENVIRONMENTAL
& NUTRITIONAL FACTORS
Concluding Remarks
Metabonomics is a powerful top-down systems biology tool for
investigating drug toxicity, disease processes, phenotypic variation &
differential gene function in vivo.
NOVEL OUTPUTS:
Metabolic biomarker information on system regulation & failure.
Deeper understanding of DISEASE MECHANISMS.
Models that incorporate genetic & environmental interactions.
Omics data must be considered in an extensive biological framework with
robust statistical interrogation and integration to visualize system activity
Analyzing modulations of the MICROBE-MAMMAL-METABOLIC AXIS will
be crucial for understanding genotype-phenotype interactions and
variation in toxicity and efficacy of drugs in man.
Top down metabolic modeling is likely to prove to be a powerful tool in
the pursuit of Personalized Healthcare Solutions and understanding the
Health of Nations.
The Metabonomics Engine @ IC & Collaborators
Academic: Dr Elaine Holmes, Prof John C. Lindon, Dr H. Keun Dr
T.Ebbels, Dr J. Bundy, Prof James Scott, Prof Tim Aitman, Prof Paul
Elliot, Dr H. Tang, Dr G. Tranter, Dr S. Mitchell. (Imperial)
Post Doctoral Group: Drs, O. Cloarec, M. Dumas, A. Craig, A. Maher, B.
Beckwith-Hall, E. A. Clayton, R. Barton, J., Y. Wang, E. Meibaum, I.
Douarte, S. Bruce. T. Tseng, C.Stella, M. Coen. J. Sidhu, E.Skiordi, M.
Bollard, ………..etc
Graduate Students: T. Athersuch, I. Yap, R. C. Bailey, C. Teague, D.
Parker, A. Tregay. J. Pearce, J. Bowen, S. Lowdell, L.Smith, A. Cooray,
N.Jones, G. McLaughlin, D. O’Connor, R.Liu, M.Ratalainen, K. Veselkov,
F.P. Martin. …etc
Collaborators: Dr Rob Plum, John Shockcor (WATERS), Prof Ian D.
Wilson and Dr T.Orton (AZ ), Prof J. Everett, Drs M. Reily and D.
Robertson, (Pfizer), Prof Jose Ordovas (Tufts University), Prof Burt
Singer (Princeton University), Drs M. Spraul (Bruker), Dr Sunil Kochhar
(Nestle), Frans van D’Ouderra,J. Powell, M. Faughan et al (Unilever).
Dr D. Gaugier (Oxford University), Prof D.Withers (UCL).
FUNDING: NIH, The Wellcome Trust, BBSRC, MRC, EPSRC, NERC, The
Royal Society, Roche Foundation, Servier, Lilly, P&G, Pfizer,
AstraZeneca, Nestle, Unilever, Novo Nordisk, Roche Foundation, BMS,
Hi-Q, Metabometrix, METAGRAD, WATERS CORPORATION.